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Glama

L2beat

by io.github.pipeworx-io

Resolve Entity

resolve_entity
Read-onlyIdempotent

"What's the ticker for…" / "find the CIK for…" / "what's the LEI for…" / "what's the RxCUI for…" / "look up the ID for…" / "what is X's official identifier" / "who owns X" / "is X a subsidiary of Y" — resolve a user-spoken NAME to the canonical/official identifiers other tools require as input. Use FIRST whenever you have a name but need an ID. SUPPORTED TYPES: "company" (cross-source identity spine: 10-digit CIK + ticker + company_name from SEC EDGAR, legal-entity LEI from GLEIF with parent/ultimate-parent/children ownership when the LEI resolves, and security FIGI from OpenFIGI — by exact ticker map when a ticker is implied, and otherwise by name search, so NON-EQUITY instruments that never have a ticker (municipal and corporate bonds, notes, authority debt) DO resolve here; when a name matches more than one instrument it asserts nothing and returns figi_candidates to pick from, which is the correct answer to an issuer name that does not identify a single bond; every identifier is labelled with the source that established it, and an identifier that could NOT be resolved is stated explicitly under unresolved rather than omitted — accepts ticker, CIK, ISIN, or company name as input; an ISIN like "CH0038863350" resolves to the LEGAL ENTITY that issued the security via the GLEIF ISIN-to-LEI mapping, covering non-US issuers EDGAR cannot reach), "drug" (returns RxCUI + ingredient + brand from RxNorm + pipeworx://rxnorm/concept/{rxcui} citation; accepts brand or generic name). LEI/FIGI enrichment degrades gracefully — if GLEIF or OpenFIGI is unavailable, the EDGAR identifiers still return. Each call cascades through several lookup endpoints internally — using resolve_entity replaces 2-3 manual lookups.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
typeYesEntity type: "company" or "drug".
valueYesFor company: ticker (AAPL), CIK (0000320193), or name. For drug: brand or generic name (e.g., "ozempic", "metformin"). Pass the ENTITY NAME ONLY — for a bond that is the ISSUER exactly as printed ("NEW YORK ST DORM AUTH"), never the question's full noun phrase ("NEW YORK ST DORM AUTH revenue bonds"): the FIGI lookup matches instrument names, so trailing security-class words match nothing.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed1 schema field changed
    • changedInput schema / properties / value / description
      Previous value: -"For company: ticker (AAPL), CIK (0000320193), or name. For drug: brand or generic name (e.g., \"ozempic\", \"metformin\")."New value: +"For company: ticker (AAPL), CIK (0000320193), or name. For drug: brand or generic name (e.g., \"ozempic\", \"metformin\"). Pass the ENTITY NAME ONLY — for a bond that is the ISSUER exactly as printed (\"NEW YORK ST DORM AUTH\"), never the question's full noun phrase (\"NEW YORK ST DORM AUTH revenue bonds\"): the FIGI lookup matches instrument names, so trailing security-class words match nothing."
  2. First observed

TDQS

A4.7/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations declare read-only, open-world, and idempotent behavior, and the description reinforces it without contradiction. It discloses important behavioral traits: ambiguous matches return 'figi_candidates' rather than asserting, unresolved identifiers are listed explicitly, enrichment degrades gracefully, and each call internally cascades through multiple lookup endpoints. This goes well beyond the annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is long but dense, and the most critical guidance is front-loaded through examples and the 'Use FIRST' directive. Some sections are wordy and could be tightened, but every major sentence contributes behavioral or usage information. It is appropriately structured for the tool's complexity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

There is no output schema, so the description carries the full burden of explaining return expectations. It covers resolved identifiers, sources, ambiguous outcomes, unresolved cases, and degraded behavior. For a 2-parameter lookup tool with rich external-system behavior, this is complete enough for an agent to invoke it correctly and interpret responses appropriately.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, but the description adds substantial meaning beyond the schema: the exact 'entity name only' rule, the warning about trailing security-class words for bonds, valid input forms per entity type, and the ISIN-to-LEI mapping example. This is high-value guidance that an agent cannot derive from the schema alone.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb and resource: resolve user-spoken names to canonical/official identifiers. It includes concrete usage examples, names the supported entity types ('company', 'drug'), and clarifies the scope of each type (SEC EDGAR, GLEIF, OpenFIGI, RxNorm). This clearly distinguishes the tool from generic or sibling tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly says 'Use FIRST whenever you have a name but need an ID' and explains that other tools require these identifiers as input. It gives detailed guidance for bond lookup, warning against passing full noun phrases and requiring the issuer name only. It does not explicitly name sibling alternatives or state when not to use it, but the context is strong.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

A3.6/5.0
Disambiguation2/5

The tool set mixes L2beat-specific tools with a large number of unrelated Pipeworx, Polymarket, and other domain tools. While individual descriptions are clear, the overall set forces agents to navigate many irrelevant tools, causing confusion about the server's primary purpose.

Naming Consistency2/5

Tool names follow inconsistent conventions: snake_case (list_projects), camelCase (ask_pipeworx), and mixed patterns (generate_llms_txt, bet_research). No unified naming pattern exists across the set, reducing predictability.

Tool Count1/5

With 35 tools, the count is far too high for an L2beat server. Only about 5-6 tools are actually related to L2 scaling; the rest are from other domains, making the tool surface bloated and unfocused.

Completeness2/5

The L2beat-specific tools cover basic operations (list projects, get project, TVS, activity) but lack important features like bridge analysis, project comparisons, or detailed risk breakdowns. The numerous unrelated tools do not address gaps in L2 functionality.

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